## 사전실행코드
import polars as pl
import plotly.io as pio
pio.renderers.default = "notebook_connected"
import plotly.express as px
df_spotify = (pl.read_csv("./universal_top_spotify_songs.csv", try_parse_dates = True,
null_values = [""])
    .filter(pl.col('snapshot_date').dt.year() == 2024).sort('snapshot_date'))

df_spotify = (
    df_spotify.with_columns(pl.when(pl.col('country').is_null() == True) ## country 열이 null이면
        .then(pl.lit('WW')) ## WW로 변경
        .otherwise(pl.col('country')).alias('country')) ## 아니면 원래대로
    .drop_nulls()) ## 그 외 null이 들어간 행 삭제

key_levels = pl.Enum(["C", "C#", "D", "Eb", "E", "F", "F#", "G", "G#", "A", "Bb", "B"])

df_spotify = (df_spotify.with_columns(pl.col('key').cast(pl.String)
    .replace(["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"],
        ["C", "C#", "D", "Eb", "E", "F", "F#", "G", "G#", "A", "Bb", "B"]))
    .with_columns(pl.col('key').cast(key_levels)).sort('key'))

df_spotify = (df_spotify.with_columns(pl.col('artists').str.split(', ')) ## ,를 기준으로 문자열을 분리
    ## 리스트의 첫 번째 아이템을 가져와서 main_vocal로 저장
    .with_columns(pl.col('artists').list.get(0, null_on_oob = True).alias('main_vocal'),
        pl.col('artists').list.tail(-1).alias('featuring')) ## 첫 번째 아이템을 제외한 나머지를featuring으로 저장
    .with_columns(pl.when(pl.col('featuring').list.len() == 0) ## 리스트 길이가 0이면
        .then(None) ## None으로 설정
        .otherwise(pl.col('featuring')).name.keep())) ## 아니면 그대로 유지

import pycountry_convert as pc

def get_continent_name(nation_code: str) -> str:
    if nation_code != 'WW':
        continent_code = pc.country_alpha2_to_continent_code(nation_code)
    else:
        continent_code = 'WW'
    continent_dict = {"NA": "North America","SA": "South America", "AS": "Asia", "AF": "Africa",
        "OC": "Oceania", "EU": "Europe", "AQ": "Antarctica", "WW": "Global"}
    return continent_dict[continent_code]

df_spotify = (df_spotify.with_columns(pl.col('country')
    ## 앞서 정의한 함수를 country 열에 적용
    .map_elements(get_continent_name, return_dtype = pl.String).alias('continent')))

df_spotify.select(pl.col(pl.Int64), pl.col(pl.Float64)).corr()

fig = px.imshow((df_spotify.select(pl.col(pl.Int64), pl.col(pl.Float64)).corr().with_columns(pl.
all().round(1))),
    y = df_spotify.select(pl.col(pl.Int64), pl.col(pl.Float64)).columns,
    text_auto = True, aspect = "auto", color_continuous_scale = "RdBu_r")
fig.show()

(df_spotify.select(pl.col(pl.Int64), pl.col(pl.Float64)).corr()
    .with_columns(index = pl.lit(pl.Series(df_spotify.select(pl.col(pl.Int64), pl.col(pl.Float64)).
columns)))
    .unpivot(index = 'index')
    .filter(pl.col('index') != pl.col('variable'))
    .filter((pl.col('value') > 0.5) | (pl.col('value') < -0.5))
    .sort('value', descending = True))

fig = px.scatter(df_spotify.sample(fraction = 0.1, seed = 123),
    x = 'loudness', y = 'energy', trendline = 'ols', trendline_color_override = "red", opacity = 0.1,
    range_y = [0, 1])
fig.show()

result = px.get_trendline_results(fig)
result.px_fit_results.iloc[0].summary()

fig = px.scatter(df_spotify.sample(fraction = 0.1, seed = 123),
    x = 'acousticness', y = 'energy', trendline_color_override = "red", trendline = "ols",
opacity = 0.1)
fig.show()

result = px.get_trendline_results(fig)
result.px_fit_results.iloc[0].summary()

expr_1 = pl.col('name').unique().len().over('main_vocal')
df_spotify_EDA1 = (
pl.concat([
    ## 글로벌 메인보컬 Top 10 산출
    (df_spotify.filter(pl.col('country') == 'WW').select(pl.col('main_vocal').alias('Global_Main_Vocal'),
        expr_1.alias('Global_Songs')).unique().sort('Global_Songs', descending = True).head(10)),
    ## 우리나라 메인보컬 Top 10 산출
    (df_spotify.filter(pl.col('country') == 'KR').select(pl.col('main_vocal').alias('KR_Main_Vocal'),
        expr_1.alias('KR_Songs')).unique().sort('KR_Songs', descending = True).head(10)),
    ## 미국 메인보컬 Top 10 산출
    (df_spotify.filter(pl.col('country') == 'US').select(pl.col('main_vocal').alias('US_Main_Vocal'),
        expr_1.alias('US_Songs')).unique().sort('US_Songs', descending = True).head(10)),
    ## 영국 메인보컬 Top 10 산출
    (df_spotify.filter(pl.col('country') == 'GB').select(pl.col('main_vocal').alias('GB_Main_Vocal'),
        expr_1.alias('GB_Songs')).unique().sort('GB_Songs', descending = True).head(10))],
    how = 'horizontal')
    .with_columns(pl.int_range(1, 11).alias('rank'))
    .select(pl.col('rank'), pl.all().exclude('rank')))
df_spotify_EDA1

fig = px.bar(
    (df_spotify.filter(pl.col('country').is_in(["WW", "KR", "US", "GB"])) ## 대상 국가 필터링
        .group_by('country', 'main_vocal') ## 국가와 메인보컬로 그룹화
        .agg(pl.col('name').unique().len()) ## 노래 이름의 고윳값에 대한 개수 산출
        .sort(['country', 'name'], descending = True) ## 결과를 국가와 노래 이름으로 정렬
        .group_by('country', maintain_order = True) ## 결과를 국가명으로 다시 그룹화
        .head(10)), ## 상위 10개만 선택
    ## X축은 main_vocal, Y축과 막대 텍스트는 name으로 설정
    x = 'main_vocal', y = 'name', text = 'name',
    facet_row = 'country', facet_row_spacing = 0.07, ## facet 설정
    labels = {"main_vocal": "메인보컬", "name": "노래수"}) ## 축 라벨 설정
fig.update_xaxes(matches = None, showticklabels = True) ## X축 간의 매칭과 틱라벨을 제거
fig.show()

(df_spotify.filter(pl.col('country') == "WW", ## 글로벌만 필터링
        pl.col('main_vocal') == "Taylor Swift") ## 테일러 스위프트만 필터링
    .group_by(['main_vocal', 'name']) ## 메인보컬과 노래명으로 그룹화
    .len('chart in days') ## 전체 개수 산출
    .sort('chart in days', descending = True).head(10))

(df_spotify.filter(pl.col('country') == "KR", pl.col('main_vocal') == "Jimin")
    .group_by(['main_vocal', 'name']).len('chart in days').sort('chart in days', descending = 
True).head(10))

df_spotify_EDA2 = ( pl.concat([
    (df_spotify.filter(pl.col('country') == "WW") ## 글로벌 차트만 필터링
        .select(pl.col('name').alias('Global_Song'), ## 노래명 열 선택
            pl.col('main_vocal').alias('Global_Vocal'), ## 메인보컬 열 선택
            pl.col('name').len().over('name').alias('Global_Day')) ## 노래명별 노래 수 산출
    ## 고유 행만 산출 정렬 후 상위 10곡만 출력
    .unique().sort('Global_Day', descending = True).head(10)),
    (df_spotify.filter(pl.col('country') == "KR") ## 한국 차트만 필터링
        .select(pl.col('name').alias('KR_Song'),
            pl.col('main_vocal').alias('KR_Vocal'),
            pl.col('name').len().over('name').alias('KR_Day'))
        .unique().sort('KR_Day', descending = True).head(10)),
    (df_spotify.filter(pl.col('country') == "US") ## 미국 차트만 필터링
        .select(pl.col('name').alias('US_Song'),
            pl.col('main_vocal').alias('US_Vocal'),
            pl.col('name').len().over('name').alias('US_Day'))
        .unique().sort('US_Day', descending = True).head(10)),
    (df_spotify.filter(pl.col('country') == "GB")
        .select(pl.col('name').alias('GB_Song'), ## 영국 차트만 핕터링
            pl.col('main_vocal').alias('GB_Vocal'),
            pl.col('name').len().over('name').alias('GB_Day'))
        .unique().sort('GB_Day', descending = True).head(10))],
    how = 'horizontal')
    .with_columns(pl.int_range(1, 11).alias('rank')) ## 순위 열 생성
    .select(pl.col('rank'), pl.all().exclude('rank')) ## 순위 열을 앞으로 재배치
)
df_spotify_EDA2

(df_spotify_EDA2.style
    .tab_header(title = "2024년 노래 Top 10") ## 표 제목 설정
    .tab_stub(rowname_col = 'rank') ## 스텁 설정
    ## 스패너 설정
    .tab_spanner("글로벌", ['Global_Song', 'Global_Vocal', 'Global_Day'])
    .tab_spanner("한국", ['KR_Song', 'KR_Vocal', 'KR_Day'])
    .tab_spanner("미국", ['US_Song', 'US_Vocal', 'US_Day'])
    .tab_spanner("영국", ['GB_Song', 'GB_Vocal', 'GB_Day'])
    ## 열 정렬 설정
    .cols_align(align = "center")
    ## 열 라벨 설정
    .cols_label(Global_Song = "노래", Global_Vocal = "메인보컬", Global_Day = "차트일수",
        KR_Song = "노래", KR_Vocal = "메인보컬", KR_Day = "차트일수",
        US_Song = "노래", US_Vocal = "메인보컬", US_Day = "차트일수",
        GB_Song = "노래", GB_Vocal = "메인보컬", GB_Day = "차트일수"))

## 국가별, 메인보컬별, 노래별 차트일 수 붙이기
(pl.concat([
    (df_spotify.filter(pl.col('country') == "WW", pl.col('daily_rank') == 1)
        .group_by('name')
        .agg(pl.col('main_vocal').first().alias('Global_Main_Vocal'),
        pl.len().alias('Global_Chart_Days'))
        .rename({"name": "Global_Song"}).sort('Global_Chart_Days', descending = True).head(10)),
    (df_spotify.filter(pl.col('country') == "KR", pl.col('daily_rank') == 1)
        .group_by('name')
        .agg(pl.col('main_vocal').first().alias('KR_Main_Vocal'), pl.len().alias('KR_Chart_Days'))
        .rename({"name": "KR_Song"}).sort('KR_Chart_Days', descending = True).head(10)),
    (df_spotify.filter(pl.col('country') == "US", pl.col('daily_rank') == 1)
        .group_by('name')
        .agg(pl.col('main_vocal').first().alias('US_Main_Vocal'), pl.len().alias('US_Chart_Days'))
        .rename({"name": "US_Song"}).sort('US_Chart_Days', descending = True).head(10)),
    (df_spotify.filter(pl.col('country') == "GB", pl.col('daily_rank') == 1)
        .group_by('name')
        .agg(pl.col('main_vocal').first().alias('GB_Main_Vocal'), pl.len().alias('GB_Chart_Days'))
        .rename({"name": "GB_Song"}).sort('GB_Chart_Days', descending = True).head(10))],
    how = 'horizontal')
    .with_columns(pl.int_range(1, 11).alias('rank')) ## 순위 열 만들기
    .select(pl.col('rank'), pl.all().exclude('rank')).style ## 순위 열 순서 설정
    .tab_header(title = "2024년 차트 1위 노래 Top 10") ## 표 제목 설정
    .tab_stub(rowname_col = 'rank')
    ## 스패너 설정
    .tab_spanner("글로벌", ['Global_Song', 'Global_Main_Vocal', 'Global_Chart_Days'])
    .tab_spanner("한국", ['KR_Song', 'KR_Main_Vocal', 'KR_Chart_Days'])
    .tab_spanner("미국", ['US_Song', 'US_Main_Vocal', 'US_Chart_Days'])
    .tab_spanner("영국", ['GB_Song', 'GB_Main_Vocal', 'GB_Chart_Days'])
.cols_align(align = "center") ## 열 제목 정렬
    ## 열 라벨 설정
    .cols_label(Global_Song = "노래", Global_Main_Vocal = "메인보컬", Global_Chart_Days = "차트일수",
        KR_Song = "노래", KR_Main_Vocal = "메인보컬", KR_Chart_Days = "차트일수",
        US_Song = "노래", US_Main_Vocal = "메인보컬", US_Chart_Days = "차트일수",
        GB_Song = "노래", GB_Main_Vocal = "메인보컬", GB_Chart_Days = "차트일수"))

(df_spotify.filter(pl.col('country').is_in(["WW", "KR", "US", "GB"]), ## 국가 필터링
        pl.col('daily_rank') == 1) ## 1위만 필터링
    ## 국가별 1위곡 수 산출
    .select(pl.col('country'), pl.col('name').unique().len().over('country'))
    .unique().sort('name', descending = True))

fig = px.line((df_spotify.
    ## 우리나라 1위곡들만 필터링
    filter(pl.col('name').is_in(["Like Crazy", "Who", "Magnetic", "How Sweet", "Supernova",
"Supernatural"]),
        pl.col('country') == 'KR')),
    x = 'snapshot_date', y = 'daily_rank', color = 'name', line_dash = 'name',
    labels = {"snapshot_date": "날짜", "daily_rank": "순위", "name": "노래"})
fig.update_yaxes(autorange = "reversed")
fig.show()

(df_spotify.filter(pl.col('name') == "APT.", pl.col('daily_rank') == 1)
    .select(pl.col('continent'), pl.col('country').unique().len().over('continent').alias('NO.1'))
    .unique().sort('NO.1', descending = True))

(df_spotify.filter(pl.col('name') == "APT.", pl.col('daily_rank') == 1,
    pl.col('continent') == "Global").select(pl.col('snapshot_date')))

(df_spotify.filter(pl.col('country') == "KR", pl.col('name') == "APT.")
.select(pl.col('daily_rank').min()))

fig = px.line((df_spotify.filter(pl.col('country').is_in(["WW", "KR", "US", "GB"]),
pl.col('name') == "APT.")),
    x = 'snapshot_date', y = 'daily_rank', color = 'country', line_dash = 'country')
fig.update_yaxes(autorange = "reversed")
fig.show()

df_spotify_EDA4 = (
    df_spotify.filter(pl.col('name') == "APT.").select(pl.col('country'), pl.col('continent'),
        pl.col('country').map_elements(
            lambda x: pc.country_name_to_country_alpha3(pc.country_alpha2_to_country_name(x))
                if x != "WW" else "WW", return_dtype = pl.String).alias('nation'),
                    (pl.col('country').map_elements(
                        lambda x: pc.country_alpha2_to_country_name(x) if x != "WW" else "WW",
                            return_dtype = pl.String).alias('nation_name')),
        pl.col('popularity'), pl.col('daily_rank'), pl.col('name').len().over('country').
alias('chart_days'))
            .group_by('nation').agg(pl.col('country').first(),
        pl.col('nation_name').first(), pl.col('continent').first(),
        pl.col('popularity').mean(), pl.col('daily_rank').mean(),
        pl.col('chart_days').first()))
df_spotify_EDA4.sort('daily_rank')

df_spotify.select(pl.col('country').unique()).join(df_spotify_EDA4, on = 'country', how = "anti")

fig = px.choropleth(df_spotify_EDA4, locations = 'nation', color = 'popularity', scope = "world",
    hover_name = 'nation_name', color_continuous_scale = "greens", width = 800, height = 600,
    title = "로제의 APT. 인기도")
fig.show()

fig = px.choropleth(df_spotify_EDA4, locations = 'nation', color = 'daily_rank', scope = "world",
    hover_name = 'nation_name', color_continuous_scale = "greens_r", width = 800, height = 600,
    title = "로제의 APT. 평균 순위")
fig.show()

daily_rank	daily_movement	weekly_movement	popularity	duration_ms	mode	time_signature	danceability	energy	loudness	speechiness	acousticness	instrumentalness	liveness	valence	tempo
f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
1.0	-0.145062	-0.264775	-0.112801	0.036033	0.011965	0.027519	-0.062886	-0.042519	-0.044374	0.004615	0.040069	0.013488	0.030228	-0.057007	0.018175
-0.145062	1.0	0.368973	-0.156114	0.009213	0.009721	0.00442	-0.017507	-0.00345	-0.018784	0.000933	0.000971	0.013345	0.004058	-0.003661	-0.002467
-0.264775	0.368973	1.0	-0.176558	0.009557	0.010423	0.008576	-0.029831	-0.00634	-0.025519	0.019179	-0.000584	0.008384	0.012274	-0.007706	0.000593
-0.112801	-0.156114	-0.176558	1.0	0.015487	0.103172	-0.133768	-0.033194	0.015687	0.136663	-0.192961	-0.111156	-0.010133	-0.056468	-0.008356	0.001929
0.036033	0.009213	0.009557	0.015487	1.0	0.043542	0.033176	-0.196576	-0.119117	-0.135113	-0.115064	0.040664	0.02811	-0.024323	-0.200502	-0.025502
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
0.040069	0.000971	-0.000584	-0.111156	0.040664	0.034684	-0.104765	-0.230753	-0.527654	-0.435017	-0.029584	1.0	0.017645	-0.013343	-0.156258	-0.083366
0.013488	0.013345	0.008384	-0.010133	0.02811	0.036304	0.013961	-0.006804	-0.080463	-0.220426	-0.05598	0.017645	1.0	0.006919	-0.091602	-0.006889
0.030228	0.004058	0.012274	-0.056468	-0.024323	-0.002485	0.00932	-0.112974	0.141553	0.056927	0.058599	-0.013343	0.006919	1.0	-0.003821	0.05631
-0.057007	-0.003661	-0.007706	-0.008356	-0.200502	-0.08638	0.012608	0.417625	0.358156	0.283396	0.043455	-0.156258	-0.091602	-0.003821	1.0	0.031509
0.018175	-0.002467	0.000593	0.001929	-0.025502	0.022574	-0.067961	-0.183312	0.10058	0.048295	0.075679	-0.083366	-0.006889	0.05631	0.031509	1.0

index	variable	value
str	str	f64
"loudness"	"energy"	0.724866
"energy"	"loudness"	0.724866
"acousticness"	"energy"	-0.527654
"energy"	"acousticness"	-0.527654

Dep. Variable:	y	R-squared:	0.528
Model:	OLS	Adj. R-squared:	0.528
Method:	Least Squares	F-statistic:	1.434e+05
Date:	Sat, 06 Dec 2025	Prob (F-statistic):	0.00
Time:	19:03:05	Log-Likelihood:	1.0000e+05
No. Observations:	128158	AIC:	-2.000e+05
Df Residuals:	128156	BIC:	-2.000e+05
Df Model:	1
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	0.9488	0.001	1138.448	0.000	0.947	0.950
x1	0.0460	0.000	378.679	0.000	0.046	0.046

Omnibus:	408.453	Durbin-Watson:	0.490
Prob(Omnibus):	0.000	Jarque-Bera (JB):	508.583
Skew:	-0.058	Prob(JB):	3.65e-111
Kurtosis:	3.286	Cond. No.	18.8

10.2 수치형 변수 간 상관관계 회귀분석하기¶

10.3 스포티파이 데이터로 글로벌 인기도 파악하기¶

Dep. Variable:	y	R-squared:	0.274
Model:	OLS	Adj. R-squared:	0.274
Method:	Least Squares	F-statistic:	4.839e+04
Date:	Sat, 06 Dec 2025	Prob (F-statistic):	0.00
Time:	19:03:12	Log-Likelihood:	72408.
No. Observations:	128158	AIC:	-1.448e+05
Df Residuals:	128156	BIC:	-1.448e+05
Df Model:	1
Covariance Type:	nonrobust

Omnibus:	1380.436	Durbin-Watson:	0.984
Prob(Omnibus):	0.000	Jarque-Bera (JB):	1356.962
Skew:	-0.231	Prob(JB):	2.18e-295
Kurtosis:	2.798	Cond. No.	4.38

rank	Global_Main_Vocal	Global_Songs	KR_Main_Vocal	KR_Songs	US_Main_Vocal	US_Songs	GB_Main_Vocal	GB_Songs
i64	str	u32	str	u32	str	u32	str	u32
1	"Taylor Swift"	36	"Jimin"	21	"Taylor Swift"	37	"Taylor Swift"	36
2	"Kendrick Lamar"	17	"Lim Young Woong"	19	"Future"	35	"Oasis"	29
3	"Beyoncé"	17	"aespa"	16	"Beyoncé"	23	"Kanye West"	20
4	"Future"	16	"DAY6"	16	"Kendrick Lamar"	20	"Beyoncé"	19
5	"Sabrina Carpenter"	15	"NewJeans"	15	"Kanye West"	18	"Eminem"	19
6	"Kanye West"	15	"Jung Kook"	13	"Zach Bryan"	18	"Kendrick Lamar"	16
7	"Tyler"	15	"V"	12	"Ariana Grande"	17	"Ariana Grande"	16
8	"Eminem"	14	"LE SSERAFIM"	11	"Post Malone"	17	"Tyler"	15
9	"Ariana Grande"	14	"YANGHONGWON"	10	"Eminem"	17	"Sabrina Carpenter"	15
10	"Billie Eilish"	11	"Taylor Swift"	10	"Tyler"	16	"Charli xcx"	14

main_vocal	name	chart in days
str	str	u32
"Taylor Swift"	"Cruel Summer"	290
"Taylor Swift"	"Fortnight (feat. Post Malone)"	119
"Taylor Swift"	"I Can Do It With a Broken Hear…	76
"Taylor Swift"	"Down Bad"	32
"Taylor Swift"	"Who’s Afraid of Little Old Me?"	27
"Taylor Swift"	"Guilty as Sin?"	27
"Taylor Swift"	"So Long, London"	24
"Taylor Swift"	"But Daddy I Love Him"	24
"Taylor Swift"	"My Boy Only Breaks His Favorit…	24
"Taylor Swift"	"Florida!!! (feat. Florence + T…	16

main_vocal	name	chart in days
str	str	u32
"Jimin"	"Closer Than This"	353
"Jimin"	"Like Crazy"	352
"Jimin"	"Set Me Free Pt.2"	201
"Jimin"	"Like Crazy (English Version)"	201
"Jimin"	"Alone"	180
"Jimin"	"Face-off"	179
"Jimin"	"Smeraldo Garden Marching Band …	176
"Jimin"	"Who"	155
"Jimin"	"Slow Dance (feat. Sofia Carson…	155
"Jimin"	"Be Mine"	155

rank	Global_Song	Global_Vocal	Global_Day	KR_Song	KR_Vocal	KR_Day	US_Song	US_Vocal	US_Day	GB_Song	GB_Vocal	GB_Day
i64	str	str	u32	str	str	u32	str	str	u32	str	str	u32
1	"One Of The Girls (with JENNIE,…	"The Weeknd"	341	"Closer Than This"	"Jimin"	353	"Stick Season"	"Noah Kahan"	326	"Stick Season"	"Noah Kahan"	327
2	"I Wanna Be Yours"	"Arctic Monkeys"	341	"3D (feat. Jack Harlow)"	"Jung Kook"	353	"Lose Control"	"Teddy Swims"	325	"Lose Control"	"Teddy Swims"	320
3	"Lose Control"	"Teddy Swims"	340	"Seven (feat. Latto) (Explicit …	"Jung Kook"	353	"I Remember Everything (feat. K…	"Zach Bryan"	322	"Beautiful Things"	"Benson Boone"	309
4	"Beautiful Things"	"Benson Boone"	328	"Standing Next to You"	"Jung Kook"	352	"Something in the Orange"	"Zach Bryan"	317	"Mr. Brightside"	"The Killers"	300
5	"The Night We Met"	"Lord Huron"	324	"Like Crazy"	"Jimin"	352	"Beautiful Things"	"Benson Boone"	313	"Cruel Summer"	"Taylor Swift"	272
6	"Cruel Summer"	"Taylor Swift"	290	"Love Me Again"	"V"	336	"Last Night"	"Morgan Wallen"	272	"Scared To Start"	"Michael Marcagi"	266
7	"LUNA"	"Feid"	276	"Grain of Sand"	"Lim Young Woong"	327	"See You Again (feat. Kali Uchi…	"Tyler"	263	"Too Sweet"	"Hozier"	244
8	"End of Beginning"	"Djo"	266	"Do or Die"	"Lim Young Woong"	322	"Good Luck, Babe!"	"Chappell Roan"	247	"Unwritten"	"Natasha Bedingfield"	243
9	"we can't be friends (wait for …	"Ariana Grande"	255	"London Boy"	"Lim Young Woong"	299	"Espresso"	"Sabrina Carpenter"	241	"The Night We Met"	"Lord Huron"	232
10	"Too Sweet"	"Hozier"	251	"Polaroid"	"Lim Young Woong"	282	"A Bar Song (Tipsy)"	"Shaboozey"	239	"Good Luck, Babe!"	"Chappell Roan"	229

	글로벌			한국			미국			영국
2024년 노래 Top 10
	노래	메인보컬	차트일수	노래	메인보컬	차트일수	노래	메인보컬	차트일수	노래	메인보컬	차트일수
1	One Of The Girls (with JENNIE, Lily Rose Depp)	The Weeknd	341	Closer Than This	Jimin	353	Stick Season	Noah Kahan	326	Stick Season	Noah Kahan	327
2	I Wanna Be Yours	Arctic Monkeys	341	3D (feat. Jack Harlow)	Jung Kook	353	Lose Control	Teddy Swims	325	Lose Control	Teddy Swims	320
3	Lose Control	Teddy Swims	340	Seven (feat. Latto) (Explicit Ver.)	Jung Kook	353	I Remember Everything (feat. Kacey Musgraves)	Zach Bryan	322	Beautiful Things	Benson Boone	309
4	Beautiful Things	Benson Boone	328	Standing Next to You	Jung Kook	352	Something in the Orange	Zach Bryan	317	Mr. Brightside	The Killers	300
5	The Night We Met	Lord Huron	324	Like Crazy	Jimin	352	Beautiful Things	Benson Boone	313	Cruel Summer	Taylor Swift	272
6	Cruel Summer	Taylor Swift	290	Love Me Again	V	336	Last Night	Morgan Wallen	272	Scared To Start	Michael Marcagi	266
7	LUNA	Feid	276	Grain of Sand	Lim Young Woong	327	See You Again (feat. Kali Uchis)	Tyler	263	Too Sweet	Hozier	244
8	End of Beginning	Djo	266	Do or Die	Lim Young Woong	322	Good Luck, Babe!	Chappell Roan	247	Unwritten	Natasha Bedingfield	243
9	we can't be friends (wait for your love)	Ariana Grande	255	London Boy	Lim Young Woong	299	Espresso	Sabrina Carpenter	241	The Night We Met	Lord Huron	232
10	Too Sweet	Hozier	251	Polaroid	Lim Young Woong	282	A Bar Song (Tipsy)	Shaboozey	239	Good Luck, Babe!	Chappell Roan	229

	글로벌			한국			미국			영국
2024년 차트 1위 노래 Top 10
	노래	메인보컬	차트일수	노래	메인보컬	차트일수	노래	메인보컬	차트일수	노래	메인보컬	차트일수
1	Die With A Smile	Lady Gaga	95	Like Crazy	Jimin	170	Not Like Us	Kendrick Lamar	48	Stick Season	Noah Kahan	73
2	Beautiful Things	Benson Boone	36	Who	Jimin	155	Taste	Sabrina Carpenter	35	Espresso	Sabrina Carpenter	65
3	Espresso	Sabrina Carpenter	33	Magnetic	ILLIT	11	CARNIVAL	Kanye West	34	Taste	Sabrina Carpenter	59
4	BIRDS OF A FEATHER	Billie Eilish	24	How Sweet	NewJeans	9	Please Please Please	Sabrina Carpenter	27	Please Please Please	Sabrina Carpenter	23
5	La Diabla	Xavi	21	Supernova	aespa	7	Lovin On Me	Jack Harlow	23	Last Christmas	Wham!	20
6	Who	Jimin	20	Supernatural	NewJeans	1	That’s So True	Gracie Abrams	18	Too Sweet	Hozier	17
7	APT.	ROSÉ	19	None	None	None	Too Sweet	Hozier	17	That’s So True	Gracie Abrams	13
8	Please Please Please	Sabrina Carpenter	19	None	None	None	Rockin' Around The Christmas Tree	Brenda Lee	16	BACKBONE	Chase & Status	9
9	i like the way you kiss me	Artemas	13	None	None	None	Die With A Smile	Lady Gaga	16	Sailor Song	Gigi Perez	8
10	MILLION DOLLAR BABY	Tommy Richman	11	None	None	None	Good Luck, Babe!	Chappell Roan	13	Beautiful Things	Benson Boone	8

nation	country	nation_name	continent	popularity	daily_rank	chart_days
str	str	str	str	f64	f64	u32
"SGP"	"SG"	"Singapore"	"Asia"	90.301587	1.238095	63
"HKG"	"HK"	"Hong Kong"	"Asia"	90.301587	1.301587	63
"MYS"	"MY"	"Malaysia"	"Asia"	90.301587	1.31746	63
"TWN"	"TW"	"Taiwan, Province of China"	"Asia"	90.301587	2.31746	63
"ARE"	"AE"	"United Arab Emirates"	"Asia"	90.301587	2.412698	63
…	…	…	…	…	…	…
"PRY"	"PY"	"Paraguay"	"South America"	86.742857	40.742857	35
"COL"	"CO"	"Colombia"	"South America"	93.485714	41.4	35
"DOM"	"DO"	"Dominican Republic"	"North America"	89.7	46.0	10
"GTM"	"GT"	"Guatemala"	"North America"	94.25	47.75	4
"GRC"	"GR"	"Greece"	"Europe"	91.0	49.0	2

snapshot_date
date
2024-10-22
2024-10-23
2024-10-24
2024-10-25
2024-10-26
…
2024-11-05
2024-11-25
2024-11-26
2024-11-27
2024-12-09

country
str
"AR"
"EG"
"BY"
"NG"
"UY"